Useful R functions

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Sandbox vectors

Let’s define some vectors which can be used for demonstrations:

manyNumbers <- sample( 1:1000, 20 )
manyNumbers

 [1] 645 410 673 713 728 652 654 511 573 691 507 535 419 558 861 837 836 493 965 249

manyNumbersWithNA <- sample( c( NA, NA, NA, manyNumbers ) )
manyNumbersWithNA

 [1] 861 728  NA 652 419 511 558 837 673 965 493 654 573 535 645  NA 410 836  NA 691 507 713 249

duplicatedNumbers <- sample( 1:5, 10, replace = TRUE )
duplicatedNumbers

 [1] 3 4 2 2 3 2 4 4 5 2

letters

 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y"
[26] "z"

LETTERS

 [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T" "U" "V" "W" "X" "Y"
[26] "Z"

mixedLetters <- c( sample( letters, 5 ), sample( LETTERS, 5 ) )
mixedLetters

 [1] "o" "f" "q" "i" "t" "S" "Z" "O" "K" "Q"

Are all/any elements TRUE

• Input: logical vector
• Output: single logical value
• Task: try, understand what happens when you use manyNumbersWithNA instead of manyNumbers.

all( manyNumbers <= 1000 )

[1] TRUE

all( manyNumbers <= 500 )

[1] FALSE

any( manyNumbers > 1000 )

[1] FALSE

any( manyNumbers > 500 )

[1] TRUE

all( !is.na( manyNumbers ) )

[1] TRUE

any( is.na( manyNumbers ) )

[1] FALSE

Which elements are TRUE

Input: logical vector Output: vector of numbers (positions)

which( manyNumbers > 900 )

[1] 19

which( manyNumbersWithNA > 900 )

[1] 10

which( is.na( manyNumbersWithNA ) )

[1]  3 16 19

Filtering vector elements

• Input: any vector and filtering condition
• Output: elements of the input vector
• Note: several ways to get the same effect

manyNumbers[ manyNumbers > 900 ] # indexing by logical vector

[1] 965

manyNumbers[ which( manyNumbers > 900 ) ] # indexing by positions

[1] 965

somePositions <- which( manyNumbers > 900 )
manyNumbers[ somePositions ]

[1] 965

Are some elements among other elements

• Input: two vectors
• Output: a logical vector corresponding to the first input vector

"A" %in% LETTERS

[1] TRUE

c( "X", "Y", "Z" ) %in% LETTERS

[1] TRUE TRUE TRUE

all( c( "X", "Y", "Z" ) %in% LETTERS )

[1] TRUE

all( mixedLetters %in% LETTERS )

[1] FALSE

any( mixedLetters %in% LETTERS )

[1] TRUE

mixedLetters[ mixedLetters %in% LETTERS ]

[1] "S" "Z" "O" "K" "Q"

mixedLetters[ !( mixedLetters %in% LETTERS ) ]

[1] "o" "f" "q" "i" "t"

manyNumbers %in% 300:600

 [1] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
[18]  TRUE FALSE FALSE

which( manyNumbers %in% 300:600 )

[1]  2  8  9 11 12 13 14 18

sum( manyNumbers %in% 300:600 )

[1] 8

Pick one of two (three) depending on condition

• Input: a logical vector and two vectors additional vectors (for TRUE, for FALSE)
• Output: elements of the additional vectors
• Note: it can take care of NAs

if_else( manyNumbersWithNA >= 500, "large", "small" )

 [1] "large" "large" NA      "large" "small" "large" "large" "large" "large" "large" "small" "large"
[13] "large" "large" "large" NA      "small" "large" NA      "large" "large" "large" "small"

if_else( manyNumbersWithNA >= 500, "large", "small", "UNKNOWN" )

 [1] "large"   "large"   "UNKNOWN" "large"   "small"   "large"   "large"   "large"   "large"   "large"  
[11] "small"   "large"   "large"   "large"   "large"   "UNKNOWN" "small"   "large"   "UNKNOWN" "large"  
[21] "large"   "large"   "small"  

# here integer 0L is needed instead of real 0.0 
# manyNumbersWithNA contains integer numbers and the method complains
if_else( manyNumbersWithNA >= 500, manyNumbersWithNA, 0L ) 

 [1] 861 728  NA 652   0 511 558 837 673 965   0 654 573 535 645  NA   0 836  NA 691 507 713   0

Duplicates and unique elements

• Input: a vector

unique( duplicatedNumbers )

[1] 3 4 2 5

unique( c( NA, duplicatedNumbers, NA ) )

[1] NA  3  4  2  5

duplicated( duplicatedNumbers )

 [1] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE

Positions of max/min elements

which.max( manyNumbersWithNA )

[1] 10

manyNumbersWithNA[ which.max( manyNumbersWithNA ) ]

[1] 965

which.min( manyNumbersWithNA )

[1] 23

manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]

[1] 249

range( manyNumbersWithNA, na.rm = TRUE )

[1] 249 965

Sorting/ordering of vectors

manyNumbersWithNA

 [1] 861 728  NA 652 419 511 558 837 673 965 493 654 573 535 645  NA 410 836  NA 691 507 713 249

sort( manyNumbersWithNA )

 [1] 249 410 419 493 507 511 535 558 573 645 652 654 673 691 713 728 836 837 861 965

sort( manyNumbersWithNA, na.last = TRUE )

 [1] 249 410 419 493 507 511 535 558 573 645 652 654 673 691 713 728 836 837 861 965  NA  NA  NA

sort( manyNumbersWithNA, na.last = TRUE, decreasing = TRUE )

 [1] 965 861 837 836 728 713 691 673 654 652 645 573 558 535 511 507 493 419 410 249  NA  NA  NA

manyNumbersWithNA[1:5]

[1] 861 728  NA 652 419

order( manyNumbersWithNA[1:5] )

[1] 5 4 2 1 3

rank( manyNumbersWithNA[1:5] )

[1] 4 3 5 2 1

sort( mixedLetters )

 [1] "f" "i" "K" "o" "O" "q" "Q" "S" "t" "Z"

Ranking of vectors

manyDuplicates <- sample( 10:15, 10, replace = TRUE )
rank( manyDuplicates )

 [1]  4.0  7.5  2.0  7.5  5.0  2.0  2.0  7.5 10.0  7.5

rank( manyDuplicates, ties.method = "min" )

 [1]  4  6  1  6  5  1  1  6 10  6

rank( manyDuplicates, ties.method = "random" )

 [1]  4  7  2  6  5  3  1  9 10  8

Rounding numbers

v <- c( -1, -0.5, 0, 0.5, 1, rnorm( 10 ) )
v

 [1] -1.00000000 -0.50000000  0.00000000  0.50000000  1.00000000 -0.75598051  0.04192044  1.28736653
 [9]  0.53801260  0.66749581  0.63383308 -1.05891116  0.62195381 -0.33191859  0.45806834

round( v, 0 )

 [1] -1  0  0  0  1 -1  0  1  1  1  1 -1  1  0  0

round( v, 1 )

 [1] -1.0 -0.5  0.0  0.5  1.0 -0.8  0.0  1.3  0.5  0.7  0.6 -1.1  0.6 -0.3  0.5

round( v, 2 )

 [1] -1.00 -0.50  0.00  0.50  1.00 -0.76  0.04  1.29  0.54  0.67  0.63 -1.06  0.62 -0.33  0.46

floor( v )

 [1] -1 -1  0  0  1 -1  0  1  0  0  0 -2  0 -1  0

ceiling( v )

 [1] -1  0  0  1  1  0  1  2  1  1  1 -1  1  0  1

Naming vector elements

heights <- c( Amy = 166, Eve = 170, Bob = 177 )
heights

Amy Eve Bob 
166 170 177 

names( heights )

[1] "Amy" "Eve" "Bob"

names( heights ) <- c( "AMY", "EVE", "BOB" )
heights

AMY EVE BOB 
166 170 177 

heights[[ "EVE" ]]

[1] 170

Generating grids

expand_grid( x = c( 1:3, NA ), y = c( "a", "b" ) )

# A tibble: 8 x 2
      x y    
  <int> <chr>
1     1 a    
2     1 b    
3     2 a    
4     2 b    
5     3 a    
6     3 b    
7    NA a    
8    NA b    

Generating combinations

combn( c( "a", "b", "c", "d", "e" ), m = 2, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  "c"  "d"  
[2,] "b"  "c"  "d"  "e"  "c"  "d"  "e"  "d"  "e"  "e"  

combn( c( "a", "b", "c", "d", "e" ), m = 3, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  
[2,] "b"  "b"  "b"  "c"  "c"  "d"  "c"  "c"  "d"  "d"  
[3,] "c"  "d"  "e"  "d"  "e"  "e"  "d"  "e"  "e"  "e"  

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